Jupyter is great. Yet, I find myself missing all the little tweaks I made to Emacs whenever I have Jupyter open in a browser. The obvious solution is to have Jupyter in Emacs. One solution is EIN, the Emacs IPython Notebook. However, I had a mixed experience with it: it would often hang and eat up memory (I never bothered to try to debug this behaviour). A neat alternative, for me, is emacs-jupyter. Here’s my setup.
fplll contains several algorithms on lattices that rely on floating-point computations. This includes implementations of the floating-point LLL reduction algorithm, offering different speed/guarantees ratios. It contains a ‘wrapper’ choosing the estimated best sequence of variants in order to provide a guaranteed output as fast as possible. In the case of the wrapper, the succession of variants is oblivious to the user. It also includes a rigorous floating-point implementation of the Kannan-Fincke-Pohst algorithm that finds a shortest non-zero lattice vector, and the BKZ reduction algorithm.
fplll is distributed under the GNU Lesser General Public License (either version 2.1 of the License, or, at your option, any later version) as published by the Free Software Foundation.
In short, fplll is your best bet at a publicly available fast lattice-reduction library and fpylll provides a convenient interface for it — for experimentation, development and extension — from Python.
For the rest of this post, I’ll give you a tour of the features currently implemented in fpylll and point out some areas where we could do with some help.
If you’ve written a fair amount of Cython code in your time, chances are that you got frustrated by
- buggy C/C++ code crashing your Python shell and
- the fact that you cannot interrupt C/C++ functions.
For example, the following Cython code cannot be interrupted:
while True: pass
On the other hand, if you have written Cython code in Sage, then you might have come across its nifty
sig_check() macros which prevent crashes and allow your calls to C/C++ code to be interrupted. Sage had signal handling — crashes, interrupts — forever (see below).
Cysignals is Sage’s signal handling reborn as a stand-alone module, i.e. it allows to wrap C/C++ code blocks in
sig_off() pairs which catch signals such as
SIGSEGV. Using it is straight-forward. Simply add
to each Cython module and then wrap long-running computations in
sig_off() pairs or check for signals with
sig_check(). See the cysignals documentation for details.
We have a first pre-release out. Pre-release because we haven’t switched Sage to the new, stand-alone code yet. Once this is done, we’ll publish version 1.0 since some version of this code has been in use on many different systems for at least decade.
Lately, I have been writing a little Python library which is aimed at managing OpenPGP encrypted mailing lists easier. In particular, it addresses the following scenario. A group of users setup a normal mailing list – say a Google group. To realise encryption all users encrypt to all users, say, by relying on Thunderbird’s/Enigmail’s “Per-Recipient Rules”. This is annoying, but doable for groups sufficiently small. However, doing all the mutual key authentications for all users would be a lot more annoying. Our users could rely on the web of trust, but many people who use encryption seem to be reluctant to publish a social graph on the Internet, so they’d rely on exchanging this information somewhat privately, e.g. on the list itself.
Hence, to make matters simpler, our mailing list might nominate a certification authority – one user they all trust who takes care of key verification and publishes signatures to those keys she verified. In the scenario I am concerned with this happens by irregular e-mails to the mailing list itself. BatzenCA is a set of Python tools to make the CA’s job easier. In particular, it helps to organise such irregular e-mails which inform users about added/removed keys – called “releases” in the package. It relies on SQLAlchemy and a patched version of PyME. I’ve been using it for a little while now and it seems to do what I want it to do. I wonder if anybody else has similar requirements where this set of tools could be useful?
Warning: While I know a little bit about cryptography and have quite a bit experience writing Python code, I am not an expert on security engineering and most software I write is rather mathematical, i.e. not aimed at practical security.